AIMC Topic: Health

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Individual health-disease phase diagrams for disease prevention based on machine learning.

Journal of biomedical informatics
Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex...

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

Journal of medical Internet research
BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical feature...

MosaicBase: A Knowledgebase of Postzygotic Mosaic Variants in Noncancer Disease-related and Healthy Human Individuals.

Genomics, proteomics & bioinformatics
Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease...

Moonstone: a novel natural language processing system for inferring social risk from clinical narratives.

Journal of biomedical semantics
BACKGROUND: Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primaril...

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.

Sensors (Basel, Switzerland)
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and ...

Identifying tweets of personal health experience through word embedding and LSTM neural network.

BMC bioinformatics
BACKGROUND: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorit...

Deep learning architectures for multi-label classification of intelligent health risk prediction.

BMC bioinformatics
BACKGROUND: Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could ...